MIoT-Based Big Data Analytics Architecture, Opportunities and Challenges for Enhanced Telemedicine Systems

Awotunde, Joseph Bamidele; Adeniyi, Abidemi Emmanuel; Ogundokun, Roseline Oluwaseun; Ajamu, Gbemisola Janet; Adebayo, Paul Olujide

doi:10.1007/978-3-030-70111-6_10

Cited by 26 publications

(7 citation statements)

References 84 publications

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“…The analysis of the achieved ratios between the BCG signal length collected during the trial, and the two signal elaboration times, made it clear that the algorithmic approach is not preferable in TM-related environments, as the amount of signal data to be elaborated demands high machine-time involved in data processing and, accordingly, an elevated computational cost. This is a strong limitation because the huge amount of data coming from remote information, signals, and image transmission that telemedicine architectures deal with is fairly comparable to many other big data-related scenarios ( 87 ). In addition, the algorithmic approach was outperformed by CNN during core elaboration in terms of both time and cost.…”

Section: Discussionmentioning

confidence: 99%

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Tramontano,

Tamburis,

Cioce

et al. 2023

Front. Digit. Health

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Medical devices (MDs) have been designed for monitoring the parameters of patients in many sectors. Nonetheless, despite being high-performing and reliable, they often turn out to be expensive and intrusive. In addition, MDs are almost exclusively used in controlled, hospital-based environments. Paving a path of technological innovation in the clinical field, a very active line of research is currently dealing with the possibility to rely on non-medical-graded low-cost devices, to develop unattended telemedicine (TM) solutions aimed at non-invasively gathering data, signals, and images. In this article, a TM solution is proposed for monitoring the heart rate (HR) of patients during sleep. A remote patient monitoring system (RPMS) featuring a smart belt equipped with pressure sensors for ballistocardiogram (BCG) signals sampling was deployed. A field trial was then conducted over a 2-month period on 24 volunteers, who also agreed to wear a finger pulse oximeter capable of producing a photoplethysmography (PPG) signal as the gold standard, to examine the feasibility of the solution via the estimation of HR values from the collected BCG signals. For this purpose, two of the highest-performing approaches for HR estimation from BCG signals, one algorithmic and the other based on a convolutional neural network (CNN), were retrieved from the literature and updated for a TM-related use case. Finally, HR estimation performances were assessed in terms of patient-wise mean absolute error (MAE). Results retrieved from the literature (controlled environment) outperformed those achieved in the experimentation (TM environment) by 29% (MAE = 4.24 vs. 5.46, algorithmic approach) and 52% (MAE = 2.32 vs. 3.54, CNN-based approach), respectively. Nonetheless, a low packet loss ratio, restrained elaboration time of the collected biomedical big data, low-cost deployment, and positive feedback from the users, demonstrate the robustness, reliability, and applicability of the proposed TM solution. In light of this, further steps will be planned to fulfill new targets, such as evaluation of respiratory rate (RR), and pattern assessment of the movement of the participants overnight.

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Section: Discussionmentioning

confidence: 99%

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Tramontano,

Tamburis,

Cioce

et al. 2023

Front. Digit. Health

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“…Like all other domains, healthcare typically involves the following stages, typically considered in the healthcare data life-cycle; these steps may change as per requirements. The life-cycle [13], [15], [18], [19], [39], [50], [78], [115], [128] usually starts with the data collection stage, followed by processing to transform and clean the data. The data analysis then involves applying such as statistical or machine learning techniques to identify patterns or insights in the data.…”

Section: A Healthcare -Big Data Life-cyclementioning

confidence: 99%

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Ahmed,

Xi,

Hou

et al. 2023

IEEE Access

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Big Data Analytics (BDA) has garnered significant attention in both academia and industries, particularly in sectors such as healthcare, owing to the exponential growth of data and advancements in technology. The integration of data from diverse sources and the utilization of advanced analytical techniques has the potential to revolutionize healthcare by improving diagnostic accuracy, enabling personalized medicine, and enhancing patient outcomes. In this paper, we aim to provide a comprehensive literature review on the application of big data analytics in healthcare, focusing on its ecosystem, applications, and data sources. To achieve this, an extensive analysis of scientific studies published between 2013 and 2023 was conducted and overall 180 scientific studies were thoroughly evaluated, establishing a strong foundation for future research and identifying collaboration opportunities in the healthcare domain. The study delves into various application areas of BDA in healthcare, highlights successful implementations, and explores their potential to enhance healthcare outcomes while reducing costs. Additionally, it outlines the challenges and limitations associated with BDA in healthcare, discusses modelling tools and techniques, showcases deployed solutions, and presents the advantages of BDA through various real-world use cases. Furthermore, this study identifies and discusses key open research challenges in the field of big data analytics in healthcare, aiming to push the boundaries and contribute to enhanced healthcare outcomes and decisionmaking processes.

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“…In addition, within the realm of issues faced by society, the prompt identification and proactive mitigation of age-related ailments within the older demographic emerge as important endeavours that are crucial for safeguarding the overall welfare of our ageing populace [2]. According to [3], the incorporation of big data analytics in the field of geriatric healthcare offers a potential opportunity to transform the comprehension, diagnosis, and eventual mitigation of these ailments. However, the existing corpus of scholarly research provides substantial evidence supporting the pressing need for sophisticated approaches in addressing the healthcare requirements of the aged population, considering the escalating impact of age-related illnesses on healthcare systems around the globe [4].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Early Detection and Prevention of Age-Related Diseases in Elderly Healthcare

Mohd Amran Mohd Daril,

Shazia Qayuum,

Alhamzah F. Abbas

et al. 2023

Int. J. Onl. Eng.

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The exponential growth of the elderly population poses considerable obstacles to healthcare systems on a global scale, hence requiring the implementation of inventive strategies to identify and mitigate age-related illnesses at an early stage. The primary objective of this study is to explore the use of big data analytics to improve healthcare practices. Specifically, the emphasis is on identifying possible risk factors and developing proactive treatments for senior citizens. The research technique used in this study is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration of 2020. This approach is utilised to ensure a thorough and transparent review of the relevant literature. Moreover, the use of Rstudio software is prevalent in the field of data processing, statistical analysis, and visualisation. By conducting a comprehensive examination of academic databases and medical literature, this study undertakes an analysis of a collection of pertinent papers to explore the significance of big data analytics in the early diagnosis and prevention of diseases in senior populations. The studies that have been chosen include a wide range of healthcare fields, such as cardiology, neurology, cancer, and geriatrics. This selection aims to provide a thorough comprehension of existing practises and identify any possible areas that may need more attention. The results of this study emphasise the significant impact that big data analytics may have on healthcare for the elderly. Using extensive and varied datasets, sophisticated analytical methodologies such as machine learning algorithms and data mining allow the detection of nuanced patterns and correlations that might function as precursors for age-related ailments.

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MIoT-Based Big Data Analytics Architecture, Opportunities and Challenges for Enhanced Telemedicine Systems

Cited by 26 publications

References 84 publications

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation

Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts

Big Data Analytics for Early Detection and Prevention of Age-Related Diseases in Elderly Healthcare

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